We select three state-of-the-art retrosynthesis single-step models to compare their performance in the multi-step domain. The model selection is based on dominant contemporary neural network approaches, i.e., contrastive learning, sequence-to-sequence, and graph-based encoding, considering their respective top-1 to top-50 performance on the USPTO-50k single-step retrosynthesis benchmark [1]}, [2]}. Accordingly, the selected models are MHNreact [3]}, a contrastive learning approach, Chemformer [4]}, a sequence-to-sequence approach, and LocalRetro [5]}, a graph-based approach. As an additional baseline, a template-based multi-layer perceptron approach [6]}, [7]}, drawing inspiration from [8]}, is included since it is often used in multi-step retrosynthesis algorithms [8]}, [10]}, [11]}, [12]}. Given that we aim to evaluate the capacity of these single-step retrosynthesis models in multi-step retrosynthesis planning, we use the model hyperparameters suggested in their respective publications (Appendix REF ) assuming the models are optimized for the single-step prediction task. The only exception is Chemformer, where we use beam size 50 to produce the single-step results and beam size 10, the publication default, for multi-step retrosynthesis planning.
